Overview

Dataset statistics

Number of variables26
Number of observations19515
Missing cells12904
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.3 MiB
Average record size in memory873.3 B

Variable types

NUM16
CAT9
URL1

Warnings

address has a high cardinality: 11171 distinct values High cardinality
subdivision has a high cardinality: 7226 distinct values High cardinality
lot has a high cardinality: 577 distinct values High cardinality
zoning has a high cardinality: 1333 distinct values High cardinality
date has a high cardinality: 5434 distinct values High cardinality
home_size has 921 (4.7%) missing values Missing
lot_size has 226 (1.2%) missing values Missing
year_built has 763 (3.9%) missing values Missing
subdivision has 1224 (6.3%) missing values Missing
lot has 1443 (7.4%) missing values Missing
estimated_value has 1393 (7.1%) missing values Missing
crime_index has 2759 (14.1%) missing values Missing
school_quality has 217 (1.1%) missing values Missing
bedrooms has 1458 (7.5%) missing values Missing
bathrooms has 1458 (7.5%) missing values Missing
sale_price has 341 (1.7%) missing values Missing
home_size is highly skewed (γ1 = 25.46239546) Skewed
lot_size is highly skewed (γ1 = 26.32136117) Skewed
bathrooms is highly skewed (γ1 = 26.23159116) Skewed
sale_price is highly skewed (γ1 = 74.34841721) Skewed
tract has 207 (1.1%) zeros Zeros
sex_offenders has 3409 (17.5%) zeros Zeros

Reproduction

Analysis started2020-11-14 04:48:04.247701
Analysis finished2020-11-14 04:49:32.839438
Duration1 minute and 28.59 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

latitude
Real number (ℝ≥0)

Distinct10342
Distinct (%)53.4%
Missing155
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean34.1240641
Minimum33.339473
Maximum34.818751
Zeros0
Zeros (%)0.0%
Memory size304.9 KiB
2020-11-13T20:49:33.144164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum33.339473
5-th percentile33.787623
Q133.974064
median34.0970995
Q334.2038865
95-th percentile34.65499
Maximum34.818751
Range1.479278
Interquartile range (IQR)0.2298225

Descriptive statistics

Standard deviation0.2343446437
Coefficient of variation (CV)0.006867430649
Kurtosis0.3028933825
Mean34.1240641
Median Absolute Deviation (MAD)0.114862
Skewness0.7783094035
Sum660641.881
Variance0.05491741202
MonotocityNot monotonic
2020-11-13T20:49:33.394980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
34.68811640.8%
 
34.6867810.4%
 
34.5293350.2%
 
34.075912300.2%
 
34.569230.1%
 
34.169819210.1%
 
34.5976200.1%
 
34.6052200.1%
 
34.402874160.1%
 
34.013935160.1%
 
33.98762150.1%
 
34.2754150.1%
 
34.16074130.1%
 
33.959944130.1%
 
34.487331130.1%
 
34.154521130.1%
 
34.458019130.1%
 
34.4863120.1%
 
34.021503120.1%
 
34.1768120.1%
 
34.173095120.1%
 
34.045091120.1%
 
33.771415120.1%
 
34.167915110.1%
 
34.465244110.1%
 
Other values (10317)1874596.1%
 
(Missing)1550.8%
 
ValueCountFrequency (%) 
33.3394733< 0.1%
 
33.339552< 0.1%
 
33.3395791< 0.1%
 
33.3399011< 0.1%
 
33.340451< 0.1%
 
33.341711< 0.1%
 
33.3422131< 0.1%
 
33.3445921< 0.1%
 
33.3543091< 0.1%
 
33.3551641< 0.1%
 
ValueCountFrequency (%) 
34.8187511< 0.1%
 
34.8070355< 0.1%
 
34.794556< 0.1%
 
34.7940871< 0.1%
 
34.790523< 0.1%
 
34.7824591< 0.1%
 
34.7787251< 0.1%
 
34.7749892< 0.1%
 
34.7537477< 0.1%
 
34.7395712< 0.1%
 

longitude
Real number (ℝ)

Distinct10367
Distinct (%)53.5%
Missing155
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean-118.275109
Minimum-118.890087
Maximum-117.6102
Zeros0
Zeros (%)0.0%
Memory size304.9 KiB
2020-11-13T20:49:33.699256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-118.890087
5-th percentile-118.6002137
Q1-118.434626
median-118.291898
Q3-118.1345722
95-th percentile-117.863048
Maximum-117.6102
Range1.279887
Interquartile range (IQR)0.30005375

Descriptive statistics

Standard deviation0.2163563729
Coefficient of variation (CV)-0.00182926378
Kurtosis-0.2068254069
Mean-118.275109
Median Absolute Deviation (MAD)0.1504745
Skewness0.2879541253
Sum-2289806.11
Variance0.04681008011
MonotocityNot monotonic
2020-11-13T20:49:33.962220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-118.06181640.8%
 
-118.2375810.4%
 
-117.9632350.2%
 
-118.140887300.2%
 
-118.0959230.1%
 
-118.1833200.1%
 
-117.8398200.1%
 
-118.4644160.1%
 
-118.4361150.1%
 
-118.5505150.1%
 
-118.043418140.1%
 
-118.1974140.1%
 
-118.47708130.1%
 
-118.447253130.1%
 
-118.531696120.1%
 
-118.155373120.1%
 
-118.531494120.1%
 
-118.170357120.1%
 
-118.389147120.1%
 
-118.517295120.1%
 
-118.19291110.1%
 
-118.196851110.1%
 
-118.492805110.1%
 
-118.385235110.1%
 
-118.167963110.1%
 
Other values (10342)1876096.1%
 
(Missing)1550.8%
 
ValueCountFrequency (%) 
-118.8900876< 0.1%
 
-118.8827531< 0.1%
 
-118.8728731< 0.1%
 
-118.8607291< 0.1%
 
-118.8552534< 0.1%
 
-118.8529171< 0.1%
 
-118.8525381< 0.1%
 
-118.8507791< 0.1%
 
-118.8505571< 0.1%
 
-118.8434284< 0.1%
 
ValueCountFrequency (%) 
-117.61021< 0.1%
 
-117.6957951< 0.1%
 
-117.6991372< 0.1%
 
-117.6995321< 0.1%
 
-117.7004551< 0.1%
 
-117.7008471< 0.1%
 
-117.7012082< 0.1%
 
-117.7014396< 0.1%
 
-117.7015141< 0.1%
 
-117.7021753< 0.1%
 

address
Categorical

HIGH CARDINALITY

Distinct11171
Distinct (%)57.4%
Missing49
Missing (%)0.3%
Memory size304.9 KiB
1428 S Marengo Ave
 
30
Vac/tampa Ave/vic Avenue H13
 
16
Vac/denmore Ave/vic Avenue H13
 
16
4342 Redwood Ave # C208
 
15
135 E Mountain View St
 
11
Other values (11166)
19378 
ValueCountFrequency (%) 
1428 S Marengo Ave300.2%
 
Vac/tampa Ave/vic Avenue H13160.1%
 
Vac/denmore Ave/vic Avenue H13160.1%
 
4342 Redwood Ave # C208150.1%
 
135 E Mountain View St110.1%
 
1625 Cherry Ave # 1110.1%
 
Vac/tampa Ave/vic Avenue H14100.1%
 
11938 Sierra Sky Dr100.1%
 
4117 Abner St100.1%
 
26809 Brookhollow Dr100.1%
 
8400 De Longpre Ave # 213100.1%
 
612 N Irena Ave # G100.1%
 
Vac/denmore Ave/vic Avenue H14100.1%
 
419 N Chester Ave100.1%
 
1488 N Kings Rd9< 0.1%
 
710 E 107th St9< 0.1%
 
44940 17th St W9< 0.1%
 
655 Museum Dr9< 0.1%
 
129 W Palm Ave9< 0.1%
 
2254 Bancroft Ave9< 0.1%
 
28301 Willow Ct9< 0.1%
 
23400 W Moon Shadows Dr9< 0.1%
 
8410 Elizabeth Ave9< 0.1%
 
1148 Hoffman Ave9< 0.1%
 
23515 Lyons Ave # 1259< 0.1%
 
Other values (11146)1918898.3%
 
(Missing)490.3%
 
2020-11-13T20:49:34.281978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique7790 ?
Unique (%)40.0%
2020-11-13T20:49:34.526729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length37
Median length17
Mean length17.71022291
Min length3

Overview of Unicode Properties

Unique unicode characters66
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
5504515.9%
 
e206556.0%
 
1174295.0%
 
a149424.3%
 
2135153.9%
 
t131013.8%
 
r127223.7%
 
n119123.4%
 
o104243.0%
 
0103653.0%
 
l102783.0%
 
399422.9%
 
v93782.7%
 
493132.7%
 
585072.5%
 
i83182.4%
 
A80312.3%
 
S78932.3%
 
d65471.9%
 
663081.8%
 
856481.6%
 
755161.6%
 
s49561.4%
 
949341.4%
 
#38591.1%
 
Other values (41)5607716.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter14801142.8%
 
Decimal Number9147726.5%
 
Space Separator5504515.9%
 
Uppercase Letter4637613.4%
 
Other Punctuation46831.4%
 
Dash Punctuation23< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
11742919.1%
 
21351514.8%
 
01036511.3%
 
3994210.9%
 
4931310.2%
 
585079.3%
 
663086.9%
 
856486.2%
 
755166.0%
 
949345.4%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
55045100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A803117.3%
 
S789317.0%
 
D33897.3%
 
C32807.1%
 
W28176.1%
 
B26255.7%
 
E20374.4%
 
R20194.4%
 
L20004.3%
 
P18994.1%
 
M17023.7%
 
V15213.3%
 
N14113.0%
 
H12412.7%
 
G10832.3%
 
O7501.6%
 
F7371.6%
 
T6881.5%
 
K4951.1%
 
J3160.7%
 
I1890.4%
 
Q1060.2%
 
Y780.2%
 
Z390.1%
 
U240.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e2065514.0%
 
a1494210.1%
 
t131018.9%
 
r127228.6%
 
n119128.0%
 
o104247.0%
 
l102786.9%
 
v93786.3%
 
i83185.6%
 
d65474.4%
 
s49563.3%
 
h36712.5%
 
c33192.2%
 
u31372.1%
 
y27531.9%
 
m25481.7%
 
g19491.3%
 
w17701.2%
 
k15621.1%
 
b13410.9%
 
p11080.7%
 
f9520.6%
 
z3050.2%
 
x2270.2%
 
j790.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
#385982.4%
 
/82417.6%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-23100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin19438756.2%
 
Common15122843.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
5504536.4%
 
11742911.5%
 
2135158.9%
 
0103656.9%
 
399426.6%
 
493136.2%
 
585075.6%
 
663084.2%
 
856483.7%
 
755163.6%
 
949343.3%
 
#38592.6%
 
/8240.5%
 
-23< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e2065510.6%
 
a149427.7%
 
t131016.7%
 
r127226.5%
 
n119126.1%
 
o104245.4%
 
l102785.3%
 
v93784.8%
 
i83184.3%
 
A80314.1%
 
S78934.1%
 
d65473.4%
 
s49562.5%
 
h36711.9%
 
D33891.7%
 
c33191.7%
 
C32801.7%
 
u31371.6%
 
W28171.4%
 
y27531.4%
 
B26251.4%
 
m25481.3%
 
E20371.0%
 
R20191.0%
 
L20001.0%
 
Other values (27)2163511.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII345615100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
5504515.9%
 
e206556.0%
 
1174295.0%
 
a149424.3%
 
2135153.9%
 
t131013.8%
 
r127223.7%
 
n119123.4%
 
o104243.0%
 
0103653.0%
 
l102783.0%
 
399422.9%
 
v93782.7%
 
493132.7%
 
585072.5%
 
i83182.4%
 
A80312.3%
 
S78932.3%
 
d65471.9%
 
663081.8%
 
856481.6%
 
755161.6%
 
s49561.4%
 
949341.4%
 
#38591.1%
 
Other values (41)5607716.2%
 

property_type
Categorical

Distinct49
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size304.9 KiB
Single Family Residence
11636 
Condominium
4500 
Duplex (2 units, any combination)
 
645
Planned Unit Development (PUD)
 
582
Residential - Vacant Land
 
518
Other values (44)
1634 
ValueCountFrequency (%) 
Single Family Residence1163659.6%
 
Condominium450023.1%
 
Duplex (2 units, any combination)6453.3%
 
Planned Unit Development (PUD)5823.0%
 
Residential - Vacant Land5182.7%
 
Apartment house (5+ units)3151.6%
 
Triplex (3 units, any combination)2221.1%
 
Quadplex (4 Units, Any Combination)2141.1%
 
MISCELLANEOUS (Vacant Land) 1390.7%
 
Light Industrial (10% Improved Office space; Machine Shop)900.5%
 
Office Building890.5%
 
Store, Retail Outlet 840.4%
 
MISCELLANEOUS (Commercial)560.3%
 
Parking Garage, Parking Structure550.3%
 
Gas Station490.3%
 
Warehouse, Storage470.2%
 
Restaurant430.2%
 
Mobile home390.2%
 
Industrial - Vacant Land350.2%
 
Vacant Commercial330.2%
 
Parcel Number130.1%
 
Store/Office (mixed use)130.1%
 
Hospital - Private100.1%
 
Recreational/Entertainment (general)8< 0.1%
 
Hotel/Motel8< 0.1%
 
Other values (24)720.4%
 
2020-11-13T20:49:34.783524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique7 ?
Unique (%)< 0.1%
2020-11-13T20:49:35.017927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length58
Median length23
Mean length21.2844991
Min length7

Overview of Unicode Properties

Unique unicode characters62
Unique unicode categories9 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e5311912.8%
 
i5055312.2%
 
n4214310.1%
 
349108.4%
 
l267116.4%
 
m230115.5%
 
a185264.5%
 
d184874.5%
 
s143263.4%
 
c138293.3%
 
o128003.1%
 
y127483.1%
 
S124033.0%
 
R123083.0%
 
g120872.9%
 
F116452.8%
 
u74641.8%
 
t66121.6%
 
C50281.2%
 
(23180.6%
 
)23180.6%
 
p23010.6%
 
D18140.4%
 
U15780.4%
 
r15350.4%
 
Other values (37)147933.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter32041677.1%
 
Uppercase Letter5135212.4%
 
Space Separator349108.4%
 
Open Punctuation23180.6%
 
Close Punctuation23180.6%
 
Other Punctuation15820.4%
 
Decimal Number15760.4%
 
Dash Punctuation5800.1%
 
Math Symbol3150.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S1240324.2%
 
R1230824.0%
 
F1164522.7%
 
C50289.8%
 
D18143.5%
 
U15783.1%
 
P13232.6%
 
L11762.3%
 
V7431.4%
 
A7281.4%
 
O4730.9%
 
I4190.8%
 
E4040.8%
 
M3620.7%
 
T2320.5%
 
Q2140.4%
 
N2110.4%
 
G1090.2%
 
B970.2%
 
W590.1%
 
H260.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e5311916.6%
 
i5055315.8%
 
n4214313.2%
 
l267118.3%
 
m230117.2%
 
a185265.8%
 
d184875.8%
 
s143264.5%
 
c138294.3%
 
o128004.0%
 
y127484.0%
 
g120873.8%
 
u74642.3%
 
t66122.1%
 
p23010.7%
 
r15350.5%
 
b11460.4%
 
x10940.3%
 
h7210.2%
 
v6930.2%
 
f3850.1%
 
k121< 0.1%
 
w3< 0.1%
 
z1< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
34910100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(2318100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
264540.9%
 
531520.0%
 
322214.1%
 
421413.6%
 
1905.7%
 
0905.7%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+315100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)2318100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,133884.6%
 
%905.7%
 
;905.7%
 
/543.4%
 
:80.5%
 
.20.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-580100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin37176889.5%
 
Common4359910.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e5311914.3%
 
i5055313.6%
 
n4214311.3%
 
l267117.2%
 
m230116.2%
 
a185265.0%
 
d184875.0%
 
s143263.9%
 
c138293.7%
 
o128003.4%
 
y127483.4%
 
S124033.3%
 
R123083.3%
 
g120873.3%
 
F116453.1%
 
u74642.0%
 
t66121.8%
 
C50281.4%
 
p23010.6%
 
D18140.5%
 
U15780.4%
 
r15350.4%
 
P13230.4%
 
L11760.3%
 
b11460.3%
 
Other values (20)70951.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
3491080.1%
 
(23185.3%
 
)23185.3%
 
,13383.1%
 
26451.5%
 
-5801.3%
 
53150.7%
 
+3150.7%
 
32220.5%
 
42140.5%
 
1900.2%
 
0900.2%
 
%900.2%
 
;900.2%
 
/540.1%
 
:8< 0.1%
 
.2< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII415367100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e5311912.8%
 
i5055312.2%
 
n4214310.1%
 
349108.4%
 
l267116.4%
 
m230115.5%
 
a185264.5%
 
d184874.5%
 
s143263.4%
 
c138293.3%
 
o128003.1%
 
y127483.1%
 
S124033.0%
 
R123083.0%
 
g120872.9%
 
F116452.8%
 
u74641.8%
 
t66121.6%
 
C50281.2%
 
(23180.6%
 
)23180.6%
 
p23010.6%
 
D18140.4%
 
U15780.4%
 
r15350.4%
 
Other values (37)147933.6%
 

home_size
Real number (ℝ≥0)

MISSING
SKEWED

Distinct3373
Distinct (%)18.1%
Missing921
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean35386.42992
Minimum3
Maximum29533680
Zeros0
Zeros (%)0.0%
Memory size304.9 KiB
2020-11-13T20:49:35.253151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile801.65
Q11200
median1584
Q32261
95-th percentile4608.7
Maximum29533680
Range29533677
Interquartile range (IQR)1061

Descriptive statistics

Standard deviation594515.6133
Coefficient of variation (CV)16.80066666
Kurtosis801.0157042
Mean35386.42992
Median Absolute Deviation (MAD)464
Skewness25.46239546
Sum657975278
Variance3.534488144e+11
MonotocityNot monotonic
2020-11-13T20:49:35.501352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1440440.2%
 
1570440.2%
 
1080430.2%
 
1176430.2%
 
1200410.2%
 
1288400.2%
 
1040370.2%
 
1240370.2%
 
1269360.2%
 
1250350.2%
 
1000350.2%
 
960340.2%
 
1430340.2%
 
1296320.2%
 
1184320.2%
 
1320320.2%
 
1380320.2%
 
1520310.2%
 
1404310.2%
 
1213310.2%
 
1328310.2%
 
1242300.2%
 
1700300.2%
 
952300.2%
 
1260300.2%
 
Other values (3348)1771990.8%
 
(Missing)9214.7%
 
ValueCountFrequency (%) 
32< 0.1%
 
1202< 0.1%
 
1801< 0.1%
 
2101< 0.1%
 
2402< 0.1%
 
2602< 0.1%
 
2802< 0.1%
 
2901< 0.1%
 
3201< 0.1%
 
3251< 0.1%
 
ValueCountFrequency (%) 
295336801< 0.1%
 
188179201< 0.1%
 
183823205< 0.1%
 
144619202< 0.1%
 
137649604< 0.1%
 
136778401< 0.1%
 
126759602< 0.1%
 
111949201< 0.1%
 
110206801< 0.1%
 
102801601< 0.1%
 

lot_size
Real number (ℝ≥0)

MISSING
SKEWED

Distinct6054
Distinct (%)31.4%
Missing226
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean3717820.035
Minimum120
Maximum1248647400
Zeros0
Zeros (%)0.0%
Memory size304.9 KiB
2020-11-13T20:49:35.809686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile3515.4
Q16013
median7798
Q320244
95-th percentile18164520
Maximum1248647400
Range1248647280
Interquartile range (IQR)14231

Descriptive statistics

Standard deviation23589198.9
Coefficient of variation (CV)6.344900688
Kurtosis918.9507802
Mean3717820.035
Median Absolute Deviation (MAD)2739
Skewness26.32136117
Sum7.171303065e+10
Variance5.564503045e+14
MonotocityNot monotonic
2020-11-13T20:49:36.118183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5000800.4%
 
2962080660.3%
 
304920650.3%
 
2482920520.3%
 
3005640520.3%
 
2265120510.3%
 
2308680490.3%
 
348480480.2%
 
2221560460.2%
 
2918520430.2%
 
261360430.2%
 
2831400410.2%
 
2787840410.2%
 
2395800410.2%
 
6000410.2%
 
3963960400.2%
 
7500390.2%
 
4573800380.2%
 
2352240380.2%
 
5400380.2%
 
3179880380.2%
 
5001370.2%
 
4443120370.2%
 
2526480370.2%
 
3876840370.2%
 
Other values (6029)1815193.0%
 
(Missing)2261.2%
 
ValueCountFrequency (%) 
1203< 0.1%
 
1794< 0.1%
 
2831< 0.1%
 
5811< 0.1%
 
7282< 0.1%
 
7455< 0.1%
 
8051< 0.1%
 
8501< 0.1%
 
8521< 0.1%
 
8621< 0.1%
 
ValueCountFrequency (%) 
12486474001< 0.1%
 
697090680130.1%
 
3588908404< 0.1%
 
3168554401< 0.1%
 
2319134401< 0.1%
 
1904007602< 0.1%
 
1810353604< 0.1%
 
1775505601< 0.1%
 
1719313201< 0.1%
 
1708423202< 0.1%
 

year_built
Real number (ℝ≥0)

MISSING

Distinct131
Distinct (%)0.7%
Missing763
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean1964.958351
Minimum1883
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size304.9 KiB
2020-11-13T20:49:36.383017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1883
5-th percentile1923
Q11949
median1963
Q31986
95-th percentile2007
Maximum2020
Range137
Interquartile range (IQR)37

Descriptive statistics

Standard deviation25.964569
Coefficient of variation (CV)0.01321380119
Kurtosis-0.63733035
Mean1964.958351
Median Absolute Deviation (MAD)18
Skewness-0.05373873432
Sum36846899
Variance674.1588434
MonotocityNot monotonic
2020-11-13T20:49:36.617951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
19505622.9%
 
19534342.2%
 
19554302.2%
 
19543822.0%
 
19523812.0%
 
19893761.9%
 
19903551.8%
 
19793541.8%
 
19563541.8%
 
19483491.8%
 
19883461.8%
 
19873401.7%
 
19513381.7%
 
19473231.7%
 
19813191.6%
 
19493151.6%
 
19803021.5%
 
19642981.5%
 
19732971.5%
 
19232761.4%
 
19862621.3%
 
19572621.3%
 
19632611.3%
 
19842601.3%
 
19592531.3%
 
Other values (106)1032352.9%
 
(Missing)7633.9%
 
ValueCountFrequency (%) 
18831< 0.1%
 
18851< 0.1%
 
18871< 0.1%
 
18903< 0.1%
 
18921< 0.1%
 
1895130.1%
 
18962< 0.1%
 
18972< 0.1%
 
18981< 0.1%
 
18991< 0.1%
 
ValueCountFrequency (%) 
2020310.2%
 
2019590.3%
 
2018720.4%
 
2017700.4%
 
2016710.4%
 
2015910.5%
 
2014590.3%
 
2013540.3%
 
2012510.3%
 
2011370.2%
 

parcel_number
Real number (ℝ≥0)

Distinct11268
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4951045567
Minimum6637311
Maximum8765024023
Zeros0
Zeros (%)0.0%
Memory size304.9 KiB
2020-11-13T20:49:36.903601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6637311
5-th percentile2162003160
Q12872010514
median5034015036
Q37066014536
95-th percentile8493041408
Maximum8765024023
Range8758386712
Interquartile range (IQR)4194004022

Descriptive statistics

Standard deviation2102549831
Coefficient of variation (CV)0.4246678409
Kurtosis-1.222535218
Mean4951045567
Median Absolute Deviation (MAD)2034996971
Skewness0.2632970357
Sum9.661965423e+13
Variance4.420715792e+18
MonotocityNot monotonic
2020-11-13T20:49:37.180356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5356007022300.2%
 
4212010102150.1%
 
7131011019110.1%
 
7261008026110.1%
 
7503022038100.1%
 
8125040022100.1%
 
6178002017100.1%
 
5215002025100.1%
 
2861060067100.1%
 
5554023031100.1%
 
60510230229< 0.1%
 
55550180059< 0.1%
 
72670010279< 0.1%
 
72720180379< 0.1%
 
62150250039< 0.1%
 
28560110469< 0.1%
 
44530300039< 0.1%
 
54660090139< 0.1%
 
56220010119< 0.1%
 
51010140079< 0.1%
 
60830010069< 0.1%
 
60380060469< 0.1%
 
32441440769< 0.1%
 
41320090399< 0.1%
 
31210280149< 0.1%
 
Other values (11243)1925398.7%
 
ValueCountFrequency (%) 
66373111< 0.1%
 
316101043< 0.1%
 
7795100161< 0.1%
 
20040090121< 0.1%
 
20040090174< 0.1%
 
20040110111< 0.1%
 
20040150181< 0.1%
 
20040150284< 0.1%
 
20050100025< 0.1%
 
20050150281< 0.1%
 
ValueCountFrequency (%) 
87650240231< 0.1%
 
87650210365< 0.1%
 
87650160121< 0.1%
 
87650130241< 0.1%
 
87650110211< 0.1%
 
87650090944< 0.1%
 
87650090413< 0.1%
 
87650060131< 0.1%
 
87640190031< 0.1%
 
87640050261< 0.1%
 

realtyID
Real number (ℝ≥0)

Distinct11265
Distinct (%)57.8%
Missing9
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1111873538
Minimum1110722482
Maximum1112630129
Zeros0
Zeros (%)0.0%
Memory size304.9 KiB
2020-11-13T20:49:37.433909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1110722482
5-th percentile1110873388
Q11111395360
median1112086022
Q31112303181
95-th percentile1112454286
Maximum1112630129
Range1907647
Interquartile range (IQR)907820.25

Descriptive statistics

Standard deviation528143.3662
Coefficient of variation (CV)0.00047500309
Kurtosis-0.9079884775
Mean1111873538
Median Absolute Deviation (MAD)269060
Skewness-0.7414015668
Sum2.168820523e+13
Variance2.789354153e+11
MonotocityNot monotonic
2020-11-13T20:49:37.680873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1111021027300.2%
 
1112108981150.1%
 
1112348330110.1%
 
1112451642110.1%
 
1112108463100.1%
 
1112469970100.1%
 
1112107613100.1%
 
1112108986100.1%
 
1112104913100.1%
 
1112240592100.1%
 
11124154829< 0.1%
 
11122258509< 0.1%
 
11122391039< 0.1%
 
11122244569< 0.1%
 
11119151649< 0.1%
 
11122209069< 0.1%
 
11123923139< 0.1%
 
11121051409< 0.1%
 
11124022059< 0.1%
 
11122019519< 0.1%
 
11119318069< 0.1%
 
11120782839< 0.1%
 
11123101659< 0.1%
 
11122598929< 0.1%
 
11120303219< 0.1%
 
Other values (11240)1924498.6%
 
ValueCountFrequency (%) 
11107224821< 0.1%
 
11107233531< 0.1%
 
11107233691< 0.1%
 
11107239731< 0.1%
 
11107245871< 0.1%
 
11107251541< 0.1%
 
11107253511< 0.1%
 
11107253711< 0.1%
 
11107264961< 0.1%
 
11107266811< 0.1%
 
ValueCountFrequency (%) 
11126301293< 0.1%
 
11125190753< 0.1%
 
11125063422< 0.1%
 
11124814272< 0.1%
 
11124813934< 0.1%
 
11124812701< 0.1%
 
11124811075< 0.1%
 
11124809933< 0.1%
 
11124808993< 0.1%
 
11124808561< 0.1%
 

county
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size304.9 KiB
Los Angeles
19506 
Ventura
 
4
Kern
 
3
Riverside
 
1
Orange
 
1
ValueCountFrequency (%) 
Los Angeles19506> 99.9%
 
Ventura4< 0.1%
 
Kern3< 0.1%
 
Riverside1< 0.1%
 
Orange1< 0.1%
 
2020-11-13T20:49:37.961011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2020-11-13T20:49:38.145159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:38.334510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length11
Mean length10.99774532
Min length4

Overview of Unicode Properties

Unique unicode characters20
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e3902218.2%
 
s3901318.2%
 
n195149.1%
 
g195079.1%
 
L195069.1%
 
o195069.1%
 
195069.1%
 
A195069.1%
 
l195069.1%
 
r9< 0.1%
 
a5< 0.1%
 
V4< 0.1%
 
t4< 0.1%
 
u4< 0.1%
 
K3< 0.1%
 
i2< 0.1%
 
O1< 0.1%
 
R1< 0.1%
 
v1< 0.1%
 
d1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter15609472.7%
 
Uppercase Letter3902118.2%
 
Space Separator195069.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
L1950650.0%
 
A1950650.0%
 
V4< 0.1%
 
K3< 0.1%
 
O1< 0.1%
 
R1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e3902225.0%
 
s3901325.0%
 
n1951412.5%
 
g1950712.5%
 
o1950612.5%
 
l1950612.5%
 
r9< 0.1%
 
a5< 0.1%
 
t4< 0.1%
 
u4< 0.1%
 
i2< 0.1%
 
v1< 0.1%
 
d1< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
19506100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin19511590.9%
 
Common195069.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e3902220.0%
 
s3901320.0%
 
n1951410.0%
 
g1950710.0%
 
L1950610.0%
 
o1950610.0%
 
A1950610.0%
 
l1950610.0%
 
r9< 0.1%
 
a5< 0.1%
 
V4< 0.1%
 
t4< 0.1%
 
u4< 0.1%
 
K3< 0.1%
 
i2< 0.1%
 
O1< 0.1%
 
R1< 0.1%
 
v1< 0.1%
 
d1< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
19506100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII214621100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e3902218.2%
 
s3901318.2%
 
n195149.1%
 
g195079.1%
 
L195069.1%
 
o195069.1%
 
195069.1%
 
A195069.1%
 
l195069.1%
 
r9< 0.1%
 
a5< 0.1%
 
V4< 0.1%
 
t4< 0.1%
 
u4< 0.1%
 
K3< 0.1%
 
i2< 0.1%
 
O1< 0.1%
 
R1< 0.1%
 
v1< 0.1%
 
d1< 0.1%
 

subdivision
Categorical

HIGH CARDINALITY
MISSING

Distinct7226
Distinct (%)39.5%
Missing1224
Missing (%)6.3%
Memory size304.9 KiB
54007
 
86
REDONDO VILLA TR
 
64
6170
 
59
1
 
50
LONG BEACH
 
45
Other values (7221)
17987 
ValueCountFrequency (%) 
54007860.4%
 
REDONDO VILLA TR640.3%
 
6170590.3%
 
1500.3%
 
LONG BEACH450.2%
 
2390.2%
 
1638360.2%
 
13240.1%
 
28676230.1%
 
1000230.1%
 
5609220.1%
 
45755210.1%
 
REDONDO BEACH210.1%
 
HERALD SECOND SUB210.1%
 
8423200.1%
 
5200.1%
 
43753200.1%
 
44328200.1%
 
6450200.1%
 
6478190.1%
 
44600190.1%
 
6180.1%
 
37064170.1%
 
6143170.1%
 
36346170.1%
 
Other values (7201)1755089.9%
 
(Missing)12246.3%
 
2020-11-13T20:49:38.604394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3813 ?
Unique (%)20.8%
2020-11-13T20:49:38.843193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length5
Mean length5.989444017
Min length1

Overview of Unicode Properties

Unique unicode characters43
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
197738.4%
 
390897.8%
 
489617.7%
 
286817.4%
 
575986.5%
 
668205.8%
 
064485.5%
 
762485.3%
 
858425.0%
 
957354.9%
 
42963.7%
 
A37283.2%
 
E33752.9%
 
R28302.4%
 
n24482.1%
 
O23292.0%
 
T23012.0%
 
N22802.0%
 
L22351.9%
 
S22321.9%
 
I15781.4%
 
D13031.1%
 
H12981.1%
 
C12361.1%
 
a12241.0%
 
Other values (18)69966.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number7519564.3%
 
Uppercase Letter3308328.3%
 
Space Separator42963.7%
 
Lowercase Letter36723.1%
 
Dash Punctuation4190.4%
 
Other Punctuation2190.2%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1977313.0%
 
3908912.1%
 
4896111.9%
 
2868111.5%
 
5759810.1%
 
668209.1%
 
064488.6%
 
762488.3%
 
858427.8%
 
957357.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n244866.7%
 
a122433.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A372811.3%
 
E337510.2%
 
R28308.6%
 
O23297.0%
 
T23017.0%
 
N22806.9%
 
L22356.8%
 
S22326.7%
 
I15784.8%
 
D13033.9%
 
H12983.9%
 
C12363.7%
 
M8232.5%
 
P8052.4%
 
B7642.3%
 
G6722.0%
 
U6712.0%
 
V5961.8%
 
W5461.7%
 
K5171.6%
 
Y4201.3%
 
F2740.8%
 
J1200.4%
 
Q590.2%
 
X460.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-419100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
4296100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
&16876.7%
 
/4018.3%
 
#115.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common8012968.6%
 
Latin3675531.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
1977312.2%
 
3908911.3%
 
4896111.2%
 
2868110.8%
 
575989.5%
 
668208.5%
 
064488.0%
 
762487.8%
 
858427.3%
 
957357.2%
 
42965.4%
 
-4190.5%
 
&1680.2%
 
/40< 0.1%
 
#11< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A372810.1%
 
E33759.2%
 
R28307.7%
 
n24486.7%
 
O23296.3%
 
T23016.3%
 
N22806.2%
 
L22356.1%
 
S22326.1%
 
I15784.3%
 
D13033.5%
 
H12983.5%
 
C12363.4%
 
a12243.3%
 
M8232.2%
 
P8052.2%
 
B7642.1%
 
G6721.8%
 
U6711.8%
 
V5961.6%
 
W5461.5%
 
K5171.4%
 
Y4201.1%
 
F2740.7%
 
J1200.3%
 
Other values (3)1500.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII116884100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
197738.4%
 
390897.8%
 
489617.7%
 
286817.4%
 
575986.5%
 
668205.8%
 
064485.5%
 
762485.3%
 
858425.0%
 
957354.9%
 
42963.7%
 
A37283.2%
 
E33752.9%
 
R28302.4%
 
n24482.1%
 
O23292.0%
 
T23012.0%
 
N22802.0%
 
L22351.9%
 
S22321.9%
 
I15781.4%
 
D13031.1%
 
H12981.1%
 
C12361.1%
 
a12241.0%
 
Other values (18)69966.0%
 

census
Real number (ℝ≥0)

Distinct8
Distinct (%)< 0.1%
Missing145
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean2.011925658
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size304.9 KiB
2020-11-13T20:49:39.029699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.104911502
Coefficient of variation (CV)0.549181078
Kurtosis1.33683195
Mean2.011925658
Median Absolute Deviation (MAD)1
Skewness1.157426755
Sum38971
Variance1.220829427
MonotocityNot monotonic
2020-11-13T20:49:39.192992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
1791640.6%
 
2605831.0%
 
3343817.6%
 
413627.0%
 
54452.3%
 
61110.6%
 
7340.2%
 
86< 0.1%
 
(Missing)1450.7%
 
ValueCountFrequency (%) 
1791640.6%
 
2605831.0%
 
3343817.6%
 
413627.0%
 
54452.3%
 
61110.6%
 
7340.2%
 
86< 0.1%
 
ValueCountFrequency (%) 
86< 0.1%
 
7340.2%
 
61110.6%
 
54452.3%
 
413627.0%
 
3343817.6%
 
2605831.0%
 
1791640.6%
 

tract
Real number (ℝ≥0)

ZEROS

Distinct2054
Distinct (%)10.6%
Missing145
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean458383.1965
Minimum0
Maximum980019
Zeros207
Zeros (%)1.1%
Memory size304.9 KiB
2020-11-13T20:49:40.036095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile113232
Q1214625
median432402
Q3620522
95-th percentile920030
Maximum980019
Range980019
Interquartile range (IQR)405897

Descriptive statistics

Standard deviation268137.0607
Coefficient of variation (CV)0.5849626747
Kurtosis-1.018318346
Mean458383.1965
Median Absolute Deviation (MAD)209802
Skewness0.3478423661
Sum8878882516
Variance7.18974833e+10
MonotocityNot monotonic
2020-11-13T20:49:40.288270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02071.1%
 
901004870.4%
 
800408630.3%
 
901205620.3%
 
800102600.3%
 
920042590.3%
 
901009550.3%
 
577501540.3%
 
910210530.3%
 
910302520.3%
 
920339520.3%
 
577603500.3%
 
141400500.3%
 
703001500.3%
 
920028500.3%
 
920330490.3%
 
900900490.3%
 
911001480.2%
 
920015480.2%
 
195100470.2%
 
910805460.2%
 
901102460.2%
 
900701450.2%
 
920031450.2%
 
900803440.2%
 
Other values (2029)1789991.7%
 
(Missing)1450.7%
 
ValueCountFrequency (%) 
02071.1%
 
33063< 0.1%
 
75114< 0.1%
 
456061< 0.1%
 
101110140.1%
 
101122170.1%
 
1012104< 0.1%
 
1012202< 0.1%
 
101300160.1%
 
101400390.2%
 
ValueCountFrequency (%) 
9800195< 0.1%
 
9800085< 0.1%
 
9303012< 0.1%
 
9301011< 0.1%
 
920339520.3%
 
920338250.1%
 
9203379< 0.1%
 
9203362< 0.1%
 
9203346< 0.1%
 
920332140.1%
 

lot
Categorical

HIGH CARDINALITY
MISSING

Distinct577
Distinct (%)3.2%
Missing1443
Missing (%)7.4%
Memory size304.9 KiB
1
2870 
2
 
695
3
 
529
4
 
424
5
 
415
Other values (572)
13139 
ValueCountFrequency (%) 
1287014.7%
 
26953.6%
 
35292.7%
 
44242.2%
 
54152.1%
 
63681.9%
 
73391.7%
 
113071.6%
 
123011.5%
 
102931.5%
 
82901.5%
 
92831.5%
 
132751.4%
 
182751.4%
 
192691.4%
 
162611.3%
 
152401.2%
 
212321.2%
 
142241.1%
 
232171.1%
 
172061.1%
 
202031.0%
 
271941.0%
 
221810.9%
 
241780.9%
 
Other values (552)800341.0%
 
(Missing)14437.4%
 
2020-11-13T20:49:40.565502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique151 ?
Unique (%)0.8%
2020-11-13T20:49:40.788275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length2
Mean length1.903048937
Min length1

Overview of Unicode Properties

Unique unicode characters28
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
1889724.0%
 
2473512.7%
 
334989.4%
 
428947.8%
 
n28867.8%
 
525426.8%
 
623506.3%
 
720855.6%
 
820215.4%
 
918855.1%
 
017624.7%
 
a14433.9%
 
A530.1%
 
C240.1%
 
B240.1%
 
D12< 0.1%
 
F5< 0.1%
 
H4< 0.1%
 
W4< 0.1%
 
Q3< 0.1%
 
I2< 0.1%
 
E2< 0.1%
 
N2< 0.1%
 
P1< 0.1%
 
R1< 0.1%
 
Other values (3)3< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number3266988.0%
 
Lowercase Letter432911.7%
 
Uppercase Letter1400.4%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1889727.2%
 
2473514.5%
 
3349810.7%
 
428948.9%
 
525427.8%
 
623507.2%
 
720856.4%
 
820216.2%
 
918855.8%
 
017625.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n288666.7%
 
a144333.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A5337.9%
 
C2417.1%
 
B2417.1%
 
D128.6%
 
F53.6%
 
H42.9%
 
W42.9%
 
Q32.1%
 
I21.4%
 
E21.4%
 
N21.4%
 
P10.7%
 
R10.7%
 
M10.7%
 
G10.7%
 
S10.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3266988.0%
 
Latin446912.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
1889727.2%
 
2473514.5%
 
3349810.7%
 
428948.9%
 
525427.8%
 
623507.2%
 
720856.4%
 
820216.2%
 
918855.8%
 
017625.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n288664.6%
 
a144332.3%
 
A531.2%
 
C240.5%
 
B240.5%
 
D120.3%
 
F50.1%
 
H40.1%
 
W40.1%
 
Q30.1%
 
I2< 0.1%
 
E2< 0.1%
 
N2< 0.1%
 
P1< 0.1%
 
R1< 0.1%
 
M1< 0.1%
 
G1< 0.1%
 
S1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII37138100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
1889724.0%
 
2473512.7%
 
334989.4%
 
428947.8%
 
n28867.8%
 
525426.8%
 
623506.3%
 
720855.6%
 
820215.4%
 
918855.1%
 
017624.7%
 
a14433.9%
 
A530.1%
 
C240.1%
 
B240.1%
 
D12< 0.1%
 
F5< 0.1%
 
H4< 0.1%
 
W4< 0.1%
 
Q3< 0.1%
 
I2< 0.1%
 
E2< 0.1%
 
N2< 0.1%
 
P1< 0.1%
 
R1< 0.1%
 
Other values (3)3< 0.1%
 

zoning
Categorical

HIGH CARDINALITY

Distinct1333
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size304.9 KiB
LAR1
2517 
LAR3
 
965
LARD1.5
 
542
LBR1N
 
433
SCUR2
 
415
Other values (1328)
14643 
ValueCountFrequency (%) 
LAR1251712.9%
 
LAR39654.9%
 
LARD1.55422.8%
 
LBR1N4332.2%
 
SCUR24152.1%
 
LARS4092.1%
 
LAR23691.9%
 
LARD23281.7%
 
LARE152481.3%
 
LARE112461.3%
 
SCUR32451.3%
 
LCA112441.3%
 
LARA2271.2%
 
LRR70001650.8%
 
LCA221600.8%
 
TORR-LO1590.8%
 
LCA211540.8%
 
LAC21530.8%
 
LARD31460.7%
 
LAR41440.7%
 
LCR1YY1200.6%
 
LKR1YY1150.6%
 
PSR61090.6%
 
LCR11060.5%
 
MNRS1030.5%
 
Other values (1308)1069354.8%
 
2020-11-13T20:49:41.020305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique368 ?
Unique (%)1.9%
2020-11-13T20:49:41.260996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length31
Median length5
Mean length5.374942352
Min length3

Overview of Unicode Properties

Unique unicode characters55
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
R1798817.1%
 
L1332912.7%
 
11058910.1%
 
A102089.7%
 
074507.1%
 
C59085.6%
 
239243.7%
 
S31963.0%
 
D31743.0%
 
Y29122.8%
 
P28782.7%
 
328002.7%
 
520151.9%
 
B19191.8%
 
M16111.5%
 
O15241.5%
 
U15241.5%
 
412921.2%
 
N11731.1%
 
711181.1%
 
E10751.0%
 
-9150.9%
 
H7770.7%
 
G7420.7%
 
67240.7%
 
Other values (30)41273.9%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter7206668.7%
 
Decimal Number3034428.9%
 
Other Punctuation9710.9%
 
Dash Punctuation9150.9%
 
Lowercase Letter4490.4%
 
Open Punctuation560.1%
 
Close Punctuation560.1%
 
Connector Punctuation35< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R1798825.0%
 
L1332918.5%
 
A1020814.2%
 
C59088.2%
 
S31964.4%
 
D31744.4%
 
Y29124.0%
 
P28784.0%
 
B19192.7%
 
M16112.2%
 
O15242.1%
 
U15242.1%
 
N11731.6%
 
E10751.5%
 
H7771.1%
 
G7421.0%
 
W5370.7%
 
T4030.6%
 
F3540.5%
 
V2760.4%
 
I2300.3%
 
K2110.3%
 
Z1010.1%
 
X16< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
11058934.9%
 
0745024.6%
 
2392412.9%
 
328009.2%
 
520156.6%
 
412924.3%
 
711183.7%
 
67242.4%
 
92160.7%
 
82160.7%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-915100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(56100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)56100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.67269.2%
 
:12713.1%
 
/868.9%
 
&808.2%
 
,60.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
t13830.7%
 
o10824.1%
 
r5412.0%
 
a429.4%
 
c306.7%
 
e255.6%
 
y245.3%
 
l122.7%
 
p122.7%
 
s20.4%
 
n10.2%
 
u10.2%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_35100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin7251569.1%
 
Common3237730.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
R1798824.8%
 
L1332918.4%
 
A1020814.1%
 
C59088.1%
 
S31964.4%
 
D31744.4%
 
Y29124.0%
 
P28784.0%
 
B19192.6%
 
M16112.2%
 
O15242.1%
 
U15242.1%
 
N11731.6%
 
E10751.5%
 
H7771.1%
 
G7421.0%
 
W5370.7%
 
T4030.6%
 
F3540.5%
 
V2760.4%
 
I2300.3%
 
K2110.3%
 
t1380.2%
 
o1080.1%
 
Z1010.1%
 
Other values (11)2190.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
11058932.7%
 
0745023.0%
 
2392412.1%
 
328008.6%
 
520156.2%
 
412924.0%
 
711183.5%
 
-9152.8%
 
67242.2%
 
.6722.1%
 
92160.7%
 
82160.7%
 
:1270.4%
 
/860.3%
 
&800.2%
 
(560.2%
 
)560.2%
 
_350.1%
 
,6< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII104892100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
R1798817.1%
 
L1332912.7%
 
11058910.1%
 
A102089.7%
 
074507.1%
 
C59085.6%
 
239243.7%
 
S31963.0%
 
D31743.0%
 
Y29122.8%
 
P28782.7%
 
328002.7%
 
520151.9%
 
B19191.8%
 
M16111.5%
 
O15241.5%
 
U15241.5%
 
412921.2%
 
N11731.1%
 
711181.1%
 
E10751.0%
 
-9150.9%
 
H7770.7%
 
G7420.7%
 
67240.7%
 
Other values (30)41273.9%
 

estimated_value
Real number (ℝ≥0)

MISSING

Distinct5350
Distinct (%)29.5%
Missing1393
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean872617.5753
Minimum104000
Maximum2999000
Zeros0
Zeros (%)0.0%
Memory size304.9 KiB
2020-11-13T20:49:41.480457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum104000
5-th percentile318230
Q1503225
median697900
Q31037000
95-th percentile2185000
Maximum2999000
Range2895000
Interquartile range (IQR)533775

Descriptive statistics

Standard deviation552076.2329
Coefficient of variation (CV)0.6326668733
Kurtosis2.196479622
Mean872617.5753
Median Absolute Deviation (MAD)239100
Skewness1.597983319
Sum1.58135757e+10
Variance3.04788167e+11
MonotocityNot monotonic
2020-11-13T20:49:41.737547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
574000240.1%
 
441000240.1%
 
1026000190.1%
 
1086000180.1%
 
1037000180.1%
 
2361000180.1%
 
615000170.1%
 
1093000170.1%
 
1064000170.1%
 
858000170.1%
 
506900170.1%
 
1108000170.1%
 
1070000170.1%
 
1125000170.1%
 
514000170.1%
 
1118000160.1%
 
439400160.1%
 
629000160.1%
 
684000160.1%
 
1007000160.1%
 
444900160.1%
 
1003000160.1%
 
403200150.1%
 
1079000150.1%
 
367200150.1%
 
Other values (5325)1769190.7%
 
(Missing)13937.1%
 
ValueCountFrequency (%) 
1040001< 0.1%
 
1070002< 0.1%
 
1250001< 0.1%
 
1310006< 0.1%
 
1350001< 0.1%
 
1470005< 0.1%
 
151000120.1%
 
1560001< 0.1%
 
1620001< 0.1%
 
1740001< 0.1%
 
ValueCountFrequency (%) 
29990005< 0.1%
 
29980001< 0.1%
 
29780003< 0.1%
 
29720004< 0.1%
 
29520006< 0.1%
 
29420002< 0.1%
 
29340001< 0.1%
 
29270002< 0.1%
 
29150002< 0.1%
 
29100001< 0.1%
 

sex_offenders
Real number (ℝ≥0)

ZEROS

Distinct110
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.37217525
Minimum0
Maximum135
Zeros3409
Zeros (%)17.5%
Memory size304.9 KiB
2020-11-13T20:49:41.980187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q38
95-th percentile25
Maximum135
Range135
Interquartile range (IQR)7

Descriptive statistics

Standard deviation13.24584803
Coefficient of variation (CV)1.796735371
Kurtosis28.8118968
Mean7.37217525
Median Absolute Deviation (MAD)3
Skewness4.735297605
Sum143868
Variance175.4524901
MonotocityNot monotonic
2020-11-13T20:49:42.242299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0340917.5%
 
1269913.8%
 
3194610.0%
 
219049.8%
 
412756.5%
 
510805.5%
 
69264.7%
 
78754.5%
 
86793.5%
 
104952.5%
 
94862.5%
 
124552.3%
 
113932.0%
 
132911.5%
 
152681.4%
 
142611.3%
 
162061.1%
 
181620.8%
 
171540.8%
 
201370.7%
 
191150.6%
 
23870.4%
 
25860.4%
 
22770.4%
 
21720.4%
 
Other values (85)9775.0%
 
ValueCountFrequency (%) 
0340917.5%
 
1269913.8%
 
219049.8%
 
3194610.0%
 
412756.5%
 
510805.5%
 
69264.7%
 
78754.5%
 
86793.5%
 
94862.5%
 
ValueCountFrequency (%) 
1351< 0.1%
 
1346< 0.1%
 
133120.1%
 
1321< 0.1%
 
129150.1%
 
1281< 0.1%
 
1221< 0.1%
 
1151< 0.1%
 
1121< 0.1%
 
1082< 0.1%
 

crime_index
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing2759
Missing (%)14.1%
Memory size304.9 KiB
Moderate
5132 
Slightly High
4871 
Low
4856 
Very Low
1322 
Moderately High
 
401
Other values (2)
 
174
ValueCountFrequency (%) 
Moderate513226.3%
 
Slightly High487125.0%
 
Low485624.9%
 
Very Low13226.8%
 
Moderately High4012.1%
 
High1650.8%
 
Very High9< 0.1%
 
(Missing)275914.1%
 
2020-11-13T20:49:42.479174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T20:49:42.620067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:42.834486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length8
Mean length7.407430182
Min length3

Overview of Unicode Properties

Unique unicode characters19
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e123978.6%
 
o117118.1%
 
t104047.2%
 
i103177.1%
 
g103177.1%
 
h103177.1%
 
l101437.0%
 
a82925.7%
 
r68644.7%
 
y66034.6%
 
66034.6%
 
L61784.3%
 
w61784.3%
 
M55333.8%
 
d55333.8%
 
n55183.8%
 
H54463.8%
 
S48713.4%
 
V13310.9%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter11459479.3%
 
Uppercase Letter2335916.2%
 
Space Separator66034.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
L617826.4%
 
M553323.7%
 
H544623.3%
 
S487120.9%
 
V13315.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e1239710.8%
 
o1171110.2%
 
t104049.1%
 
i103179.0%
 
g103179.0%
 
h103179.0%
 
l101438.9%
 
a82927.2%
 
r68646.0%
 
y66035.8%
 
w61785.4%
 
d55334.8%
 
n55184.8%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
6603100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin13795395.4%
 
Common66034.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e123979.0%
 
o117118.5%
 
t104047.5%
 
i103177.5%
 
g103177.5%
 
h103177.5%
 
l101437.4%
 
a82926.0%
 
r68645.0%
 
y66034.8%
 
L61784.5%
 
w61784.5%
 
M55334.0%
 
d55334.0%
 
n55184.0%
 
H54463.9%
 
S48713.5%
 
V13311.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
6603100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII144556100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e123978.6%
 
o117118.1%
 
t104047.2%
 
i103177.1%
 
g103177.1%
 
h103177.1%
 
l101437.0%
 
a82925.7%
 
r68644.7%
 
y66034.6%
 
66034.6%
 
L61784.3%
 
w61784.3%
 
M55333.8%
 
d55333.8%
 
n55183.8%
 
H54463.8%
 
S48713.4%
 
V13310.9%
 

enviornmental_hazards
Real number (ℝ≥0)

Distinct69
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.88716372
Minimum1
Maximum125
Zeros0
Zeros (%)0.0%
Memory size304.9 KiB
2020-11-13T20:49:43.036192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q38
95-th percentile18.3
Maximum125
Range124
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.659793616
Coefficient of variation (CV)0.9669863947
Kurtosis31.19300024
Mean6.88716372
Median Absolute Deviation (MAD)2
Skewness4.010977501
Sum134403
Variance44.35285101
MonotocityNot monotonic
2020-11-13T20:49:43.286128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3324516.6%
 
2319316.4%
 
4251312.9%
 
517398.9%
 
614467.4%
 
712906.6%
 
810455.4%
 
99074.6%
 
106443.3%
 
115082.6%
 
124022.1%
 
133832.0%
 
143221.7%
 
12271.2%
 
152171.1%
 
171600.8%
 
181560.8%
 
161420.7%
 
191310.7%
 
22800.4%
 
20730.4%
 
21720.4%
 
23710.4%
 
24510.3%
 
25430.2%
 
Other values (44)4552.3%
 
ValueCountFrequency (%) 
12271.2%
 
2319316.4%
 
3324516.6%
 
4251312.9%
 
517398.9%
 
614467.4%
 
712906.6%
 
810455.4%
 
99074.6%
 
106443.3%
 
ValueCountFrequency (%) 
1251< 0.1%
 
1231< 0.1%
 
941< 0.1%
 
911< 0.1%
 
841< 0.1%
 
831< 0.1%
 
814< 0.1%
 
801< 0.1%
 
793< 0.1%
 
771< 0.1%
 

natural_disasters
Real number (ℝ≥0)

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.199436331
Minimum0
Maximum4
Zeros169
Zeros (%)0.9%
Memory size304.9 KiB
2020-11-13T20:49:43.482502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4328303398
Coefficient of variation (CV)0.3608614552
Kurtosis1.593944361
Mean1.199436331
Median Absolute Deviation (MAD)0
Skewness1.377787019
Sum23407
Variance0.1873421031
MonotocityNot monotonic
2020-11-13T20:49:43.648210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
11538278.8%
 
2387119.8%
 
01690.9%
 
3890.5%
 
44< 0.1%
 
ValueCountFrequency (%) 
01690.9%
 
11538278.8%
 
2387119.8%
 
3890.5%
 
44< 0.1%
 
ValueCountFrequency (%) 
44< 0.1%
 
3890.5%
 
2387119.8%
 
11538278.8%
 
01690.9%
 

school_quality
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing217
Missing (%)1.1%
Memory size304.9 KiB
Average
6791 
Excellent
5295 
Above Average
4949 
Poor
2263 
ValueCountFrequency (%) 
Average679134.8%
 
Excellent529527.1%
 
Above Average494925.4%
 
Poor226311.6%
 
(Missing)2171.1%
 
2020-11-13T20:49:43.865583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T20:49:43.988108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:44.147537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length9
Mean length8.671893415
Min length3

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e3901923.1%
 
A166899.9%
 
v166899.9%
 
r140038.3%
 
a119577.1%
 
g117406.9%
 
l105906.3%
 
o94755.6%
 
n57293.4%
 
E52953.1%
 
x52953.1%
 
c52953.1%
 
t52953.1%
 
b49492.9%
 
49492.9%
 
P22631.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter14003682.7%
 
Uppercase Letter2424714.3%
 
Space Separator49492.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A1668968.8%
 
E529521.8%
 
P22639.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e3901927.9%
 
v1668911.9%
 
r1400310.0%
 
a119578.5%
 
g117408.4%
 
l105907.6%
 
o94756.8%
 
n57294.1%
 
x52953.8%
 
c52953.8%
 
t52953.8%
 
b49493.5%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
4949100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin16428397.1%
 
Common49492.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e3901923.8%
 
A1668910.2%
 
v1668910.2%
 
r140038.5%
 
a119577.3%
 
g117407.1%
 
l105906.4%
 
o94755.8%
 
n57293.5%
 
E52953.2%
 
x52953.2%
 
c52953.2%
 
t52953.2%
 
b49493.0%
 
P22631.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
4949100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII169232100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e3901923.1%
 
A166899.9%
 
v166899.9%
 
r140038.3%
 
a119577.1%
 
g117406.9%
 
l105906.3%
 
o94755.6%
 
n57293.4%
 
E52953.1%
 
x52953.1%
 
c52953.1%
 
t52953.1%
 
b49492.9%
 
49492.9%
 
P22631.3%
 

url
URL

Distinct11268
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Memory size304.9 KiB
https://www.realtytrac.com/property/ca/alhambra/91803/1428-s-marengo-ave/326696311/
 
30
https://www.realtytrac.com/property/ca/marina-del-rey/90292/4342-redwood-ave-c208/11727139/
 
15
https://www.realtytrac.com/property/ca/long-beach/90805/135-e-mountain-view-st/13046113/
 
11
https://www.realtytrac.com/property/ca/long-beach/90813/1625-cherry-ave-1/15851750/
 
11
https://www.realtytrac.com/property/ca/valencia/91355/26809-brookhollow-dr/10026911/
 
10
Other values (11263)
19438 
ValueCountFrequency (%) 
https://www.realtytrac.com/property/ca/alhambra/91803/1428-s-marengo-ave/326696311/300.2%
 
https://www.realtytrac.com/property/ca/marina-del-rey/90292/4342-redwood-ave-c208/11727139/150.1%
 
https://www.realtytrac.com/property/ca/long-beach/90805/135-e-mountain-view-st/13046113/110.1%
 
https://www.realtytrac.com/property/ca/long-beach/90813/1625-cherry-ave-1/15851750/110.1%
 
https://www.realtytrac.com/property/ca/valencia/91355/26809-brookhollow-dr/10026911/100.1%
 
https://www.realtytrac.com/property/ca/west-hollywood/90069/8400-de-longpre-ave-213/154772741/100.1%
 
https://www.realtytrac.com/property/ca/compton/90221/419-n-chester-ave/3645210/100.1%
 
https://www.realtytrac.com/property/ca/redondo-beach/90277/612-n-irena-ave-g/51848150/100.1%
 
https://www.realtytrac.com/property/ca/whittier/90601/11938-sierra-sky-dr/24825333/100.1%
 
https://www.realtytrac.com/property/ca/los-angeles/90032/4117-abner-st/44061146/100.1%
 
https://www.realtytrac.com/property/ca/lancaster/93534/44940-17th-st-w/18482976/9< 0.1%
 
https://www.realtytrac.com/property/ca/south-gate/90280/8410-elizabeth-ave/3528770/9< 0.1%
 
https://www.realtytrac.com/property/ca/w-hollywood/90069/1488-n-kings-rd/154773091/9< 0.1%
 
https://www.realtytrac.com/property/ca/los-angeles/90044/560-w-88th-st/20041553/9< 0.1%
 
https://www.realtytrac.com/property/ca/los-angeles/90065/655-museum-dr/43988548/9< 0.1%
 
https://www.realtytrac.com/property/ca/los-angeles/90061/11415-link-st/19383315/9< 0.1%
 
https://www.realtytrac.com/property/ca/long-beach/90813/1148-hoffman-ave/7953082/9< 0.1%
 
https://www.realtytrac.com/property/ca/los-angeles/90039/2254-bancroft-ave/14782211/9< 0.1%
 
https://www.realtytrac.com/property/ca/el-segundo/90245/129-w-palm-ave/23756770/9< 0.1%
 
https://www.realtytrac.com/property/ca/los-angeles/90018/2336-w-21st-st/43799452/9< 0.1%
 
https://www.realtytrac.com/property/ca/malibu/90265/23400-w-moon-shadows-dr/40252879/9< 0.1%
 
https://www.realtytrac.com/property/ca/long-beach/90813/403-w-7th-st-111/20081551/9< 0.1%
 
https://www.realtytrac.com/property/ca/santa-clarita/91350/28301-willow-ct/148001085/9< 0.1%
 
https://www.realtytrac.com/property/ca/los-angeles/90011/128-e-54th-st/1910584/9< 0.1%
 
https://www.realtytrac.com/property/ca/glendale/91201/1434-el-miradero-ave/40238785/9< 0.1%
 
Other values (11243)1925398.7%
 
ValueCountFrequency (%) 
https19515100.0%
 
ValueCountFrequency (%) 
www.realtytrac.com19515100.0%
 
ValueCountFrequency (%) 
/property/ca/alhambra/91803/1428-s-marengo-ave/326696311/300.2%
 
/property/ca/marina-del-rey/90292/4342-redwood-ave-c208/11727139/150.1%
 
/property/ca/long-beach/90813/1625-cherry-ave-1/15851750/110.1%
 
/property/ca/long-beach/90805/135-e-mountain-view-st/13046113/110.1%
 
/property/ca/whittier/90601/11938-sierra-sky-dr/24825333/100.1%
 
/property/ca/west-hollywood/90069/8400-de-longpre-ave-213/154772741/100.1%
 
/property/ca/redondo-beach/90277/612-n-irena-ave-g/51848150/100.1%
 
/property/ca/los-angeles/90032/4117-abner-st/44061146/100.1%
 
/property/ca/valencia/91355/26809-brookhollow-dr/10026911/100.1%
 
/property/ca/compton/90221/419-n-chester-ave/3645210/100.1%
 
/property/ca/long-beach/90813/1148-hoffman-ave/7953082/9< 0.1%
 
/property/ca/el-segundo/90245/129-w-palm-ave/23756770/9< 0.1%
 
/property/ca/los-angeles/90011/128-e-54th-st/1910584/9< 0.1%
 
/property/ca/south-gate/90280/8410-elizabeth-ave/3528770/9< 0.1%
 
/property/ca/valencia/91355/23515-lyons-ave-125/24610543/9< 0.1%
 
/property/ca/los-angeles/90044/560-w-88th-st/20041553/9< 0.1%
 
/property/ca/long-beach/90813/403-w-7th-st-111/20081551/9< 0.1%
 
/property/ca/lancaster/93534/44940-17th-st-w/18482976/9< 0.1%
 
/property/ca/los-angeles/90061/11415-link-st/19383315/9< 0.1%
 
/property/ca/los-angeles/90018/2336-w-21st-st/43799452/9< 0.1%
 
/property/ca/los-angeles/90002/710-e-107th-st/547403/9< 0.1%
 
/property/ca/santa-clarita/91350/28301-willow-ct/148001085/9< 0.1%
 
/property/ca/malibu/90265/23400-w-moon-shadows-dr/40252879/9< 0.1%
 
/property/ca/glendale/91201/1434-el-miradero-ave/40238785/9< 0.1%
 
/property/ca/w-hollywood/90069/1488-n-kings-rd/154773091/9< 0.1%
 
Other values (11243)1925398.7%
 
ValueCountFrequency (%) 
19515100.0%
 
ValueCountFrequency (%) 
19515100.0%
 

bedrooms
Real number (ℝ≥0)

MISSING

Distinct46
Distinct (%)0.3%
Missing1458
Missing (%)7.5%
Infinite0
Infinite (%)0.0%
Mean3.373816249
Minimum1
Maximum136
Zeros0
Zeros (%)0.0%
Memory size304.9 KiB
2020-11-13T20:49:44.390358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum136
Range135
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.631774225
Coefficient of variation (CV)1.07645881
Kurtosis530.3965628
Mean3.373816249
Median Absolute Deviation (MAD)1
Skewness19.39550493
Sum60921
Variance13.18978402
MonotocityNot monotonic
2020-11-13T20:49:44.626606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%) 
3699235.8%
 
2442322.7%
 
4385619.8%
 
511515.9%
 
18734.5%
 
63071.6%
 
81000.5%
 
7850.4%
 
10460.2%
 
12390.2%
 
9360.2%
 
28180.1%
 
11160.1%
 
16150.1%
 
18120.1%
 
14100.1%
 
157< 0.1%
 
246< 0.1%
 
136< 0.1%
 
366< 0.1%
 
995< 0.1%
 
544< 0.1%
 
204< 0.1%
 
344< 0.1%
 
254< 0.1%
 
Other values (21)320.2%
 
(Missing)14587.5%
 
ValueCountFrequency (%) 
18734.5%
 
2442322.7%
 
3699235.8%
 
4385619.8%
 
511515.9%
 
63071.6%
 
7850.4%
 
81000.5%
 
9360.2%
 
10460.2%
 
ValueCountFrequency (%) 
1363< 0.1%
 
995< 0.1%
 
961< 0.1%
 
871< 0.1%
 
801< 0.1%
 
731< 0.1%
 
691< 0.1%
 
663< 0.1%
 
601< 0.1%
 
581< 0.1%
 

bathrooms
Real number (ℝ≥0)

MISSING
SKEWED

Distinct46
Distinct (%)0.3%
Missing1458
Missing (%)7.5%
Infinite0
Infinite (%)0.0%
Mean2.71556737
Minimum1
Maximum175
Zeros0
Zeros (%)0.0%
Memory size304.9 KiB
2020-11-13T20:49:44.869773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile5
Maximum175
Range174
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.750936491
Coefficient of variation (CV)1.74951892
Kurtosis844.6315265
Mean2.71556737
Median Absolute Deviation (MAD)1
Skewness26.23159116
Sum49035
Variance22.57139754
MonotocityNot monotonic
2020-11-13T20:49:45.094949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%) 
2751338.5%
 
3509126.1%
 
1307815.8%
 
412786.5%
 
54782.4%
 
62851.5%
 
71070.5%
 
8490.3%
 
10310.2%
 
9170.1%
 
12140.1%
 
16130.1%
 
11100.1%
 
289< 0.1%
 
1756< 0.1%
 
146< 0.1%
 
206< 0.1%
 
996< 0.1%
 
175< 0.1%
 
354< 0.1%
 
154< 0.1%
 
184< 0.1%
 
133< 0.1%
 
303< 0.1%
 
253< 0.1%
 
Other values (21)340.2%
 
(Missing)14587.5%
 
ValueCountFrequency (%) 
1307815.8%
 
2751338.5%
 
3509126.1%
 
412786.5%
 
54782.4%
 
62851.5%
 
71070.5%
 
8490.3%
 
9170.1%
 
10310.2%
 
ValueCountFrequency (%) 
1756< 0.1%
 
1603< 0.1%
 
996< 0.1%
 
751< 0.1%
 
741< 0.1%
 
701< 0.1%
 
661< 0.1%
 
651< 0.1%
 
581< 0.1%
 
471< 0.1%
 

date
Categorical

HIGH CARDINALITY

Distinct5434
Distinct (%)27.9%
Missing43
Missing (%)0.2%
Memory size304.9 KiB
2020-10-30
 
621
2020-09-30
 
538
2020-10-01
 
535
2020-10-23
 
520
2020-10-02
 
455
Other values (5429)
16803 
ValueCountFrequency (%) 
2020-10-306213.2%
 
2020-09-305382.8%
 
2020-10-015352.7%
 
2020-10-235202.7%
 
2020-10-024552.3%
 
2020-09-254212.2%
 
2020-10-284172.1%
 
2020-10-214132.1%
 
2020-09-173962.0%
 
2020-09-183892.0%
 
2020-10-223862.0%
 
2020-10-273862.0%
 
2020-10-063761.9%
 
2020-09-243671.9%
 
2020-10-203581.8%
 
2020-11-023491.8%
 
2020-10-073181.6%
 
2020-10-293121.6%
 
2020-10-083091.6%
 
2020-10-263081.6%
 
2020-10-052971.5%
 
2020-10-092781.4%
 
2020-09-212581.3%
 
2020-09-282151.1%
 
2020-09-231690.9%
 
Other values (5409)1008151.7%
 
2020-11-13T20:49:45.389465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2962 ?
Unique (%)15.2%
2020-11-13T20:49:45.618525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length9.984575967
Min length3

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
05625428.9%
 
23909120.1%
 
-3894420.0%
 
12407412.4%
 
9122076.3%
 
352202.7%
 
851102.6%
 
738422.0%
 
636301.9%
 
534691.8%
 
428791.5%
 
n86< 0.1%
 
a43< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number15577679.9%
 
Dash Punctuation3894420.0%
 
Lowercase Letter1290.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
05625436.1%
 
23909125.1%
 
12407415.5%
 
9122077.8%
 
352203.4%
 
851103.3%
 
738422.5%
 
636302.3%
 
534692.2%
 
428791.8%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-38944100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n8666.7%
 
a4333.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common19472099.9%
 
Latin1290.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
05625428.9%
 
23909120.1%
 
-3894420.0%
 
12407412.4%
 
9122076.3%
 
352202.7%
 
851102.6%
 
738422.0%
 
636301.9%
 
534691.8%
 
428791.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n8666.7%
 
a4333.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII194849100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
05625428.9%
 
23909120.1%
 
-3894420.0%
 
12407412.4%
 
9122076.3%
 
352202.7%
 
851102.6%
 
738422.0%
 
636301.9%
 
534691.8%
 
428791.5%
 
n86< 0.1%
 
a43< 0.1%
 

sale_price
Real number (ℝ≥0)

MISSING
SKEWED

Distinct3882
Distinct (%)20.2%
Missing341
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean3678418.029
Minimum500
Maximum1660000000
Zeros0
Zeros (%)0.0%
Memory size304.9 KiB
2020-11-13T20:49:45.849172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile150000
Q1535000
median1160000
Q34100000
95-th percentile12500000
Maximum1660000000
Range1659999500
Interquartile range (IQR)3565000

Descriptive statistics

Standard deviation15013196.62
Coefficient of variation (CV)4.081427533
Kurtosis7822.940768
Mean3678418.029
Median Absolute Deviation (MAD)857000
Skewness74.34841721
Sum7.052998729e+10
Variance2.253960728e+14
MonotocityNot monotonic
2020-11-13T20:49:46.082181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
6500001020.5%
 
600000890.5%
 
550000830.4%
 
700000820.4%
 
500000820.4%
 
750000810.4%
 
800000760.4%
 
1500000680.3%
 
1100000670.3%
 
450000670.3%
 
1250000660.3%
 
1000000650.3%
 
850000640.3%
 
1200000630.3%
 
1600000620.3%
 
400000610.3%
 
900000600.3%
 
5500000600.3%
 
950000600.3%
 
3000000590.3%
 
2000000590.3%
 
375000590.3%
 
3500000580.3%
 
1050000570.3%
 
1300000570.3%
 
Other values (3857)1746789.5%
 
(Missing)3411.7%
 
ValueCountFrequency (%) 
5003< 0.1%
 
10003< 0.1%
 
15001< 0.1%
 
25002< 0.1%
 
30002< 0.1%
 
35003< 0.1%
 
40005< 0.1%
 
45004< 0.1%
 
50007< 0.1%
 
54181< 0.1%
 
ValueCountFrequency (%) 
16600000001< 0.1%
 
4445000001< 0.1%
 
4319700001< 0.1%
 
3500000001< 0.1%
 
1815000001< 0.1%
 
1800000001< 0.1%
 
1740000001< 0.1%
 
1660000001< 0.1%
 
1642500001< 0.1%
 
1430000002< 0.1%
 

Interactions

2020-11-13T20:48:17.924778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:18.179912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:18.397191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:18.622328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:18.840541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:19.062549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:19.266039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:19.490886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:19.703103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:20.095068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:20.311153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:20.529392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:20.735468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:20.961884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:21.180243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:21.401655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:21.616739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:21.831356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:22.047696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:22.264881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:22.481790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:22.697044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:22.895182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:23.119804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:23.326983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:23.533986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:23.744823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:23.959467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:24.158730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:24.376165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:24.588075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:24.803865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:25.005055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:25.225975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:25.450236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:25.677245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:25.898892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:26.118378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:26.492176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:26.717418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:26.931979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:27.157616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:27.378567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:27.599201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:27.850072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:28.108596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:28.425214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:28.677736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:28.947593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:29.201367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:29.443268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:29.698012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:29.951871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:30.213195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:30.454107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:30.720401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:30.967007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:31.216766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:31.470479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:31.736718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:31.968487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:32.228801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:32.496347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:32.753177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:32.972943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:33.232018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:33.549638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:33.811854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:34.035809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:34.256456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:34.484564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:34.717809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:34.930917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:35.372241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:35.595405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:35.817331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:36.016412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:36.243563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:36.457558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:36.680400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:36.880806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:37.091466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:37.285928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:37.484076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:37.690090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:37.884521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:38.069625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:38.272649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:38.465926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:38.656240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:38.851779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:39.219733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:39.567655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:39.826406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:40.080204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:40.657838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:40.968868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:41.293467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:41.541183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:41.785879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:42.014660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:42.261197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:42.644513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:42.957494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:43.746997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:44.343438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:44.804801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:45.364606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:46.315066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:46.850533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:47.521253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:47.960678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:48.255820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:48.652684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:48.957603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:49.249379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:49.545779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:50.411496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:50.836805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:51.201647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:51.504363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:51.837889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:52.103957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:52.386222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:52.674672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:52.977471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:53.251545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:53.563780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:54.427369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:55.101655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:55.438244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:55.716707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:56.003779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:56.321479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:56.588943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:56.866990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:57.270243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:57.536605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:57.764715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:58.080141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:58.405130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:58.683917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:59.013644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:59.324515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:59.546610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:48:59.842296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:00.096770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:00.358929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:00.605428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:00.874579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:01.158077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:01.407407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:01.641667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:01.850971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:02.063153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:02.277629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:02.493078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:02.715094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:02.964982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:03.237358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:03.488981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:03.711112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:03.933476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:04.153706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:04.380399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:04.597343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:04.805401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:05.121298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:05.427129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:05.764393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:06.283339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:07.262135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:07.467077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:07.685118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:07.906683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:08.146782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:08.422303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:08.761959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:09.030770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:09.376696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:09.699369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:09.921582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:10.236298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:10.472743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:10.663713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:10.918811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:11.122368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:11.324281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:11.522723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:11.738307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:12.025024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:12.225983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:12.409117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:12.636090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:12.910237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:13.190773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:13.507536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:13.873630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:14.160791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:14.443681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:14.706654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:14.961149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:15.190985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:15.422815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:15.634975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:15.863425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:16.089668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:16.315530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:16.528676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:16.741113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:16.957243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:17.177874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:17.389203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:17.607539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:17.832705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:18.055953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:18.266817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:18.475033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:18.690521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:18.902868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:19.146982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:19.385836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:19.658096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:19.904419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:20.112120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:20.346523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:20.565461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:20.799558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:21.035826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:21.273688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:21.496143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:21.734626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:21.962456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:22.178871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:22.413684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:22.640550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:23.314871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:23.554738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:23.779581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:24.010306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:24.228350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:24.435179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:24.672534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:24.941612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:25.180934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:25.403985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:25.601475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:25.818103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:26.025691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:26.269411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:26.505048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:26.742017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:26.933536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:27.145283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:27.367254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:27.570803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-13T20:49:46.335964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-13T20:49:46.753713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-13T20:49:47.160283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-13T20:49:47.572994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-11-13T20:49:47.973757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-11-13T20:49:28.881594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:30.871150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:31.729925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T20:49:32.466129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

latitudelongitudeaddressproperty_typehome_sizelot_sizeyear_builtparcel_numberrealtyIDcountysubdivisioncensustractlotzoningestimated_valuesex_offenderscrime_indexenviornmental_hazardsnatural_disastersschool_qualityurlbedroomsbathroomsdatesale_price
033.97462-118.133686224 Nye StCondominium1357.000002221560.000001988.0000063570120431111054477.00000Los Angeles453511.00000532304.000001CMR3439000.000007High151Averagehttps://www.realtytrac.com/property/ca/commerce/90040/6224-nye-st/54129244/3.000003.000002020-09-28469000.00000
134.15656-118.396894723 Laurel Canyon BlvdRestaurant2250.000009799.000001959.0000023560370341111055289.00000Los Angeles73601.00000143400.0000010LAC2nan5NaN61Excellenthttps://www.realtytrac.com/property/ca/valley-village/91607/4723-laurel-canyon-blvd/154414596/nannan2020-09-281910000.00000
234.67778-118.4511718118 Elizabeth Lake RdApartment house (5+ units)1454.0000013635.000001948.0000032420150231111055911.00000Los AngelesNaN2.00000920102.000004LCC4nan0NaN21NaNhttps://www.realtytrac.com/property/ca/lake-hughes/93532/18118-elizabeth-lake-rd/251911403/7.000006.000002020-09-28325000.00000
334.07296-118.066909259 Ramona BlvdSingle Family Residence1682.000007000.000001978.0000085940270161111055994.00000Los AngelesROSEMEAD2.00000432901.000006RMPOD752000.000003Moderate111Excellenthttps://www.realtytrac.com/property/ca/rosemead/91770/9259-ramona-blvd/154986110/3.000002.000002020-09-28738000.00000
433.77772-118.15491825 Obispo AveTriplex (3 units, any combination)1958.000006754.000001938.0000072580130161111056010.00000Los Angeles12.00000576904.0000018LBR2N989000.0000011Slightly High81Averagehttps://www.realtytrac.com/property/ca/long-beach/90804/825-obispo-ave/44027788/4.000003.000002020-09-281185000.00000
533.88632-118.251322201 W Reeve StSingle Family Residence1180.000006216.000001950.0000061400310211111056659.00000Los Angeles159817.00000543100.00000311CORL537000.0000020High141Poorhttps://www.realtytrac.com/property/ca/compton/90220/2201-w-reeve-st/140044396/3.000001.000002020-09-28560000.00000
634.19148-118.371516642 Ensign AveSingle Family Residence1296.000005843.000001944.0000023190190181111056683.00000Los Angeles131681.00000123206.000005LAR1699000.0000015Low141Averagehttps://www.realtytrac.com/property/ca/north-hollywood/91606/6642-ensign-ave/13931881/3.000002.000002020-09-28715000.00000
734.15505-118.23941622 Naranja DrQuadplex (4 Units, Any Combination)4764.000006395.000001930.0000056460160071111057331.00000Los Angeles92582.00000301900.000003GLR4YY1474000.000001Low41Above Averagehttps://www.realtytrac.com/property/ca/glendale/91206/622-naranja-dr/143877681/8.000008.000002020-09-281475000.00000
834.22908-118.601298726 Owensmouth AveApartment house (5+ units)5568.000009571.000001962.0000027790410051111057978.00000Los Angeles253163.00000113233.000005LAR3779000.000002NaN61Averagehttps://www.realtytrac.com/property/ca/canoga-park/91304/8726-owensmouth-ave/154455515/8.000008.000002020-09-281480000.00000
934.68670-118.23750Vac/oldfield St/vic 63rd StwResidential - Vacant Landnan6648.00000nan32030640321111059374.00000Los AngelesNaN4.00000920328.0000078LRR1nan2Moderate22Averagehttps://www.realtytrac.com/property/ca/lancaster/93536/vacoldfield-stvic-63rd-stw/251911299/nannan2020-09-28442000.00000

Last rows

latitudelongitudeaddressproperty_typehome_sizelot_sizeyear_builtparcel_numberrealtyIDcountysubdivisioncensustractlotzoningestimated_valuesex_offenderscrime_indexenviornmental_hazardsnatural_disastersschool_qualityurlbedroomsbathroomsdatesale_price
1950533.96914-118.183664704 Florence AveTriplex (3 units, any combination)2404.000003223440.000001959.0000062260024001111897260.00000Los Angeles1804.00000533803.0000013BLC3R631000.000008Moderate131Averagehttps://www.realtytrac.com/property/ca/bell/90201/4704-florence-ave/28419469/3.000003.000002008-07-01950000.00000
1950633.96914-118.183664704 Florence AveTriplex (3 units, any combination)2404.000003223440.000001959.0000062260024001111897260.00000Los Angeles1804.00000533803.0000013BLC3R631000.000008Moderate131Averagehttps://www.realtytrac.com/property/ca/bell/90201/4704-florence-ave/28419469/3.000003.000001999-09-22375000.00000
1950734.01489-118.177364413 Triggs StSingle Family Residence792.000006518.000001924.0000052410170011111904493.00000Los Angeles43013.00000531302.0000013LCR4251000.000006Slightly High231Averagehttps://www.realtytrac.com/property/ca/los-angeles/90040/4413-triggs-st/150183364/2.000001.000001994-04-27126000.00000
1950834.61414-118.1615940239 17th St WSingle Family Residence1820.0000011020680.000001958.0000030050120191111908002.00000Los AngelesNaN2.00000910202.0000012LCA22518700.000001Moderate31Averagehttps://www.realtytrac.com/property/ca/palmdale/93551/40239-17th-st-w/54444953/3.000002.000002020-10-19425000.00000
1950934.61414-118.1615940239 17th St WSingle Family Residence1820.0000011020680.000001958.0000030050120191111908002.00000Los AngelesNaN2.00000910202.0000012LCA22518700.000001Moderate31Averagehttps://www.realtytrac.com/property/ca/palmdale/93551/40239-17th-st-w/54444953/3.000002.000002014-10-27289000.00000
1951034.15041-118.614014523 San Feliciano DrSingle Family Residence2867.0000012572.000001962.0000020760190121111909910.00000Los Angeles234961.00000137402.0000019LARE401461000.000000Very Low21Above Averagehttps://www.realtytrac.com/property/ca/woodland-hills/91364/4523-san-feliciano-dr/30107418/4.000003.000002020-10-191285000.00000
1951134.15041-118.614014523 San Feliciano DrSingle Family Residence2867.0000012572.000001962.0000020760190121111909910.00000Los Angeles234961.00000137402.0000019LARE401461000.000000Very Low21Above Averagehttps://www.realtytrac.com/property/ca/woodland-hills/91364/4523-san-feliciano-dr/30107418/4.000003.000002017-08-181100000.00000
1951234.15041-118.614014523 San Feliciano DrSingle Family Residence2867.0000012572.000001962.0000020760190121111909910.00000Los Angeles234961.00000137402.0000019LARE401461000.000000Very Low21Above Averagehttps://www.realtytrac.com/property/ca/woodland-hills/91364/4523-san-feliciano-dr/30107418/4.000003.000002007-10-111033000.00000
1951334.15041-118.614014523 San Feliciano DrSingle Family Residence2867.0000012572.000001962.0000020760190121111909910.00000Los Angeles234961.00000137402.0000019LARE401461000.000000Very Low21Above Averagehttps://www.realtytrac.com/property/ca/woodland-hills/91364/4523-san-feliciano-dr/30107418/4.000003.000001999-04-29419000.00000
1951434.15041-118.614014523 San Feliciano DrSingle Family Residence2867.0000012572.000001962.0000020760190121111909910.00000Los Angeles234961.00000137402.0000019LARE401461000.000000Very Low21Above Averagehttps://www.realtytrac.com/property/ca/woodland-hills/91364/4523-san-feliciano-dr/30107418/4.000003.000001989-04-14470000.00000